Which is cheaper, Gemini 2.5 Pro or MAI-Code-1.1-Flash?
As of August 20, 2026, MAI-Code-1.1-Flash is 88% cheaper on blended LLM API pricing. MAI-Code-1.1-Flash costs $0.20 per million input tokens and $1.20 per million output tokens ($1.40 blended 1M-in + 1M-out). Gemini 2.5 Pro costs $1.25 / $10.00 per million tokens ($11.25 blended). Sources: https://ai.google.dev/gemini-api/docs/pricing and https://ai.azure.com. The cheapest LLM for your app still depends on how many output tokens you generate.
How much does Gemini 2.5 Pro vs MAI-Code-1.1-Flash cost per million tokens?
Gemini 2.5 Pro Gemini API pricing is $1.25 input and $10.00 output per million tokens via Google. MAI-Code-1.1-Flash Microsoft API pricing is $0.20 input and $1.20 output per million tokens via Microsoft. Use those four numbers, not ChatGPT Plus or Claude Pro subscription prices, when you are comparing APIs.
Which is better for coding, Gemini 2.5 Pro or MAI-Code-1.1-Flash?
MAI-Code-1.1-Flash leads on SWE-bench Verified: Gemini 2.5 Pro at 63.2% vs MAI-Code-1.1-Flash at 72.6%. SWE-bench measures real GitHub fixes; LiveCodeBench measures contest programming.
What is the context window for Gemini 2.5 Pro vs MAI-Code-1.1-Flash?
Gemini 2.5 Pro supports a 1M token context window, while MAI-Code-1.1-Flash supports 256K tokens. Gemini 2.5 Pro offers a larger context window. Filling a larger window bills more input tokens, so the cheaper-per-million model can still cost more on long documents.
Which model performs better on benchmarks, Gemini 2.5 Pro or MAI-Code-1.1-Flash?
MAI-Code-1.1-Flash leads on 1 of 1 shared benchmarks versus Gemini 2.5 Pro's 0 wins. Check the comparison table for GPQA Diamond, SWE-bench, MMLU, HLE, and the other evals we track. Token price and benchmark score together are the usual LLM comparison, not either number alone.
Is Gemini 2.5 Pro or MAI-Code-1.1-Flash better for production use?
Both Gemini 2.5 Pro and MAI-Code-1.1-Flash are production API models. For cost-sensitive production traffic, MAI-Code-1.1-Flash has better economics on the blended 1M-in + 1M-out scale. For maximum capability, weight the benchmark table for your domain. Many production stacks route cheap models for drafts and a frontier model for the hard turn, which is usually cheaper than sending everything to the expensive API.
Can I switch between Gemini 2.5 Pro and MAI-Code-1.1-Flash in my app?
Yes. Call Gemini 2.5 Pro through Google and MAI-Code-1.1-Flash through Microsoft with separate API keys, or through a gateway that already wraps both. Keep prompts in tokens, not characters, when you estimate the invoice. AnotherWrapper templates can swap providers without rewriting auth and billing.
How accurate is this Gemini 2.5 Pro vs MAI-Code-1.1-Flash pricing data?
List rates are the latest published Gemini API pricing and Microsoft API pricing, quoted per million tokens. Official rate sheets: https://ai.google.dev/gemini-api/docs/pricing and https://ai.azure.com. Prompt caching, batch APIs, and committed-use discounts can change the invoice. Benchmark scores are from official publications and independent evals, updated as new numbers land.
What is the output token limit for Gemini 2.5 Pro and MAI-Code-1.1-Flash?
Gemini 2.5 Pro supports up to 66K output tokens per request, while MAI-Code-1.1-Flash supports up to an unspecified number of output tokens. Output tokens are usually the expensive half of API pricing, so a higher max output cap is a capability, not a discount.
Which model has better throughput, Gemini 2.5 Pro or MAI-Code-1.1-Flash?
Gemini 2.5 Pro has a measured throughput of approximately 85 tokens/second. Throughput data for MAI-Code-1.1-Flash is not yet available. Throughput (tokens per second) and time-to-first-token are why two models with the same per-million-token price can still feel different in chat and agents.
How do I switch the models in this Gemini 2.5 Pro vs MAI-Code-1.1-Flash comparison?
Use the model pickers at the top of this LLM comparison. Search any model in the index and the URL updates. Open Gemini 2.5 Pro or MAI-Code-1.1-Flash from the sidebar for the single-model API pricing page, or go back to the LLM pricing table to sort by cheapest blended cost.
